Evelyn Xiao-Yue Gong is an Assistant Professor at Carnegie Mellon University’s Tepper School of Business, where she holds the B P Junior Faculty Chair. Her research focuses on artificial intelligence for supply chain optimization and sustainability, with additional interests in assortment optimization, pure exploration, and data-driven decision-making. Education PhD in Operations Research, Massachusetts Institute of Technology (2023) BS in Honors Mathematics, New York University (2017) BS in Interactive Media Arts, New York University (2017) Research Interests Dr. Gong’s work integrates reinforcement learning, bandit algorithms, and AI to address sustainability challenges in supply chains. Her recent publications highlight applications in cloud server deployment, sustainable packaging, and inventory management under cyclic demands. Scientific Awards Best Dissertation Prize, Supply Chain Management in the Post-Pandemic and AI Age Conference (2023) Accenture Fellowship (2022-2023) Bayer Women in Operations Research Scholarship (2021) National Championship in Splendor (2024) Professional Services Dr. Gong serves as a reviewer for journals including Management Science and NeurIPS , and has held committee roles for INFORMS awards. She has chaired sessions at the INFORMS Annual Meeting multiple times.
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Mario Bencomoمشاهده پروفایل
استادیار
Dr. Mario Bencomo is an Assistant Professor of Mathematics at California State University, Fresno. He holds a Ph.D. and M.A. in Computational and Applied Mathematics from Rice University and a B.S. in Physics and Applied Mathematics from the University of Texas at El Paso. His research focuses on numerical methods for partial differential equations, inverse problems, and scientific computing, with applications in wave propagation, heat conduction, and seismic imaging. He has published in leading computational journals and contributes to open-source scientific software. Dr. Bencomo teaches advanced mathematics courses including Numerical Analysis, Differential Equations, and Linear Algebra. He is recognized for innovative teaching pedagogy integrating technology and programming into math education. His work emphasizes practical applications such as seismic imaging, industrial cooking simulations, and efficient regularization techniques for large-scale problems. He has mentored students in projects involving reduced-order modeling, finite element methods, and spectral approaches to heat equations. His academic background includes postdoctoral research at Rice University and Brown University’s ICERM.
Emanuela Radiciمشاهده پروفایل
استادیار
Emanuela Radici is an Assistant Professor at the Università degli Studi dell'Aquila, specializing in advanced mathematical analysis with a focus on Calculus of Variations, Nonlinear PDEs, and Deterministic Particle Approximation. Her research explores topics such as optimal transport, aggregation-diffusion equations, and traffic models. She has organized workshops on Calculus of Variations and PDEs, demonstrating leadership in academic collaboration. Her work bridges theoretical analysis with applied problems, including opinion dynamics and congestion modeling. Key contributions include particle approximation methods for conservation laws and studies on BV homeomorphisms. She actively participates in international conferences and research events, contributing to the advancement of geometric methods in calculus of variations. Dr. Radici’s research output includes 22 published papers, with recent works addressing nonlinear mobilities, degenerate traffic models, and minimal extensions in geometric norms. Her interdisciplinary approach integrates functional analysis, differential geometry, and numerical methods.
Bhargav Narayanan is an Associate Professor in the Department of Mathematics at Rutgers University. His research focuses on combinatorics, probability theory, statistical physics, and theoretical computer science, with a specialization in combinatorial models and probabilistic methods. He organizes a weekly seminar at Rutgers exploring these topics. Previously, he held a research fellowship at St John's College, Cambridge, and completed his PhD under Béla Bollobás at Cambridge University. Education: PhD in Mathematics from the University of Cambridge. Career: Postdoctoral research at St John's College, Cambridge before joining Rutgers. Research Interests: Combinatorics, probability theory, statistical physics, and theoretical computer science. He has published extensively in top-tier journals such as the Annals of Mathematics and the Journal of the European Mathematical Society. Selected publications highlight contributions to antichain codes, random graph bisections, threshold phenomena, and combinatorial optimization. His work often bridges discrete mathematics and applications in physics and computer science. He has taught graduate courses on additive combinatorics, spectral methods, and probabilistic methods in combinatorics. No scientific awards explicitly listed in the provided texts. Advising: No students listed explicitly in the text. Grants: No specific grants mentioned. Lab/Team: Organizes a weekly research seminar at Rutgers.
Camillo De Lellis is a Full Professor at the Institute for Advanced Study (IAS), Princeton, specializing in geometric measure theory, calculus of variations, and partial differential equations. His work bridges analysis and geometry, addressing problems in fluid dynamics and minimal surfaces. Affiliations: Institute for Advanced Study (IBM von Neumann Professor) Education: Laurea in Mathematics (Scuola Normale Superiore, 1999), PhD (Scuola Normale Superiore, 2002).
Giuseppe Savaré is a Full Professor at Bocconi University in Milan. His research focuses on Optimal Transport, Gradient Flows, and Analysis in Metric-Measure Spaces, with contributions to the theory of rate-independent systems and calculus in non-smooth geometries. He has published extensively on topics like Wasserstein spaces, entropy-transport distances, and dissipative evolutions. Affiliations: Bocconi University (Full Professor), Member of the Calculus of Variations and PDE research group. Education: Not explicitly stated in text, inferred via academic rank and publications. His work bridges functional analysis, PDEs, and geometric measure theory. Key themes include the interplay between optimal transport and geometric structures, as well as applications to evolutionary problems in materials science and mathematical physics. Recent contributions include relaxations of unbalanced transport problems and the study of variational principles in metric measure spaces. He has organized workshops on Optimal Transport and Geometric Analysis, and his research has been supported by institutions like the Simons Foundation. His articles explore both theoretical foundations (e.g., gradient flows in Wasserstein spaces) and applied aspects (e.g., crack propagation models).
Matthias Lieroمشاهده پروفایل
پژوهشگر
Matthias Liero is the acting head of the Partial Differential Equations Research Group at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) , Berlin, and a member of the Berlin Mathematical School (BMS) Postdoctoral Faculty. His research focuses on variational methods for evolution equations , continuum mechanics , modeling semiconductor devices , and optimal transport . He leads projects funded by MATH+, including strain engineering for functional heterostructures and modeling battery electrodes with mechanical interactions. Teaching responsibilities at Humboldt University include advanced courses on optimal transport , calculus of variations , and partial differential equations . He collaborates extensively on interdisciplinary projects, such as electrothermal modeling of organic LEDs and energy-based mathematical methods for multiphase flows. His work bridges applied mathematics, physics, and engineering, emphasizing thermodynamically consistent models and rigorous analytical methods. Notable contributions include developing p(x)-Laplace thermistor models for organic semiconductor devices and advancing the Hellinger-Kantorovich distance in optimal transport theory. Current research explores nonlinear PDEs, phase transitions, and material degradation in energy systems.
- Machine Learning
- Optimization
- Stochastic Processes
- +۳ مورد دیگر
Dr. Ya-Ping Hsieh is a Lecturer at the Department of Computer Science at ETH Zürich, affiliated with the Institute for Machine Learning. His research focuses on advanced optimization techniques, stochastic processes, and their applications in machine learning. Key areas include non-convex optimization, diffusion models, and optimal transport theory. His work often addresses convergence properties of algorithms and their theoretical guarantees in complex systems. Dr. Hsieh's recent publications explore topics such as entropy-maximizing exploration via diffusion models, Riemannian stochastic optimization frameworks, and the dynamics of min-max algorithms. His contributions bridge theoretical foundations and practical implementations, emphasizing rigorous mathematical analysis. No scientific awards or grants are explicitly listed in the provided materials. He is part of the Institute for Machine Learning at ETH Zürich, contributing to both academic and applied research in computational methods.
Maria Han Veigaمشاهده پروفایل
استادیار
Maria Han Veiga is an Assistant Professor at The Ohio State University, located in Columbus, Ohio. Her research focuses on the intersection of machine learning, numerical methods, and computational physics. She holds an office at MW 550, 231 W. 18th Ave. Her research interests span machine learning theory and applications, numerical methods for PDEs (e.g., ADER schemes, spectral difference methods), astrophysical flows, and interdisciplinary AI applications in science. She has explored topics like RBF approximation, reinforcement learning, and privacy in social networks. Recent work emphasizes high-order numerical schemes, machine learning-driven algorithms for scientific simulations, and leveraging AI for astrophysical and planetary dynamics. Her publications reflect a balance between theoretical advancements and practical implementations in computational science. Maria has no listed scientific awards but demonstrates active involvement in cutting-edge research. Her advising and grants focus on machine learning applications and numerical methods, though specific details are not provided here. She is affiliated with labs or teams working on computational physics and AI-driven scientific computing.
Anton Arnold is a Professor of Mathematical Analysis at the Vienna University of Technology (TU Wien). He holds the position of Professor since 2005 and has served as Deputy Director and Head of the Institute for Analysis and Scientific Computing. His primary affiliation is with the Institute of Analysis and Scientific Computing, part of TU Wien's Faculty of Mathematics and Geoinformation. Education: Born in 1963, Arnold earned his Diploma in Technical Mathematics (1989) and Doctor of Technical Sciences (1990) from TU Wien. He completed his Habilitation in Mathematics at TU Berlin (1996). Professional roles include visiting professorships at institutions like Purdue University and Université de Provence. Research interests focus on applied mathematics, partial differential equations (PDEs), entropy methods, hypocoercivity, and quantum kinetic equations. He has led projects such as the Erasmus Mundus Master Program 'InterMaths' and the FWF-funded SFB 'Taming Complexity in Partial Differential Systems'. Teaching includes advanced courses on PDEs, variational calculus, and kinetic equations. Supervision of over 30 PhD, Master, and Bachelor students underscores his academic mentorship. His work bridges theoretical analysis with computational methods, particularly in quantum transport and wave propagation.
Professor Yasser Roudi holds the position of Professor of Disordered Systems at King’s College London’s Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. He earned his PhD from SISSA (International School for Advanced Study) in 2005 and a physics degree from Sharif University of Technology in Tehran. Prior to joining King’s, he worked at prestigious institutions like the Kavli Institute for Systems Neuroscience in Norway and NORDITA. His research focuses on theoretical neuroscience, neural networks, and the physics of disordered systems, employing mathematical tools to model information processing in brains and machines. He has been recognized with awards including the Eric Kandel Young Neuroscientist Prize (2015) and membership in the Royal Norwegian Society of Sciences and Letters. His work explores topics such as grid cells’ toroidal topology, restricted Boltzmann machines, and maximum entropy models for cortical populations. Recent publications highlight advancements in understanding neural oscillations, stochastic processes, and reinforcement learning algorithms. Roudi leads the Disordered Systems research group at King’s, contributing to the study of complex systems and statistical mechanics. Education: PhD, SISSA (2005); BSc, Sharif University of Technology (Physics). Affiliations: King’s College London, Kavli Institute for Systems Neuroscience, NORDITA, UCL, and the Institute for Advanced Study. Roudi’s research bridges theoretical physics and neuroscience, addressing fundamental questions about neural information processing and system dynamics. His awards underscore his impactful contributions to the field, while his interdisciplinary approach continues to push boundaries in understanding complex biological and artificial systems.
Vincent Vuمشاهده پروفایل
دانشیار
Vincent Vu is an Associate Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics. He holds a PhD from the University of California, Berkeley (2009). His research focuses on high-dimensional statistics, statistical machine learning, and their applications in neuroimaging, particularly fMRI. He previously worked as a software engineer in Silicon Valley for over six years, blending computational and statistical expertise. His expertise includes statistical computing, multivariate analysis, and computational sufficiency. He has been funded by the NSF and received the 2012 AISTATS Best Paper Award. He serves as an associate editor for the Journal of the American Statistical Association and The American Statistician. His work bridges statistical theory and computation, addressing challenges in high-dimensional data analysis. Collaborations involve neuroscientific applications, combining algorithmic innovation with real-world problem-solving.
- Combinatorics
- Random Graphs
- Extremal Graph Theory
- +۱۰ مورد دیگر
Dr. Demetres Christofides is an Associate Professor in Mathematics and Course Leader of BSc (Hons) Mathematics & Statistics at the University of Central Lancashire Cyprus (UCLan Cyprus). He holds a PhD in Mathematics from the University of Cambridge (2008), alongside advanced qualifications from Cambridge including a BA (2003), MA (2007), and Certificate of Advanced Study in Mathematics (2004). Affiliations: School of Sciences, Department of Mathematics. Administrative Roles: Former Senate Member (2015–2020), Deputy Head of School (2017–2018), and Academic Standards Lead (2017–2020). Research Interests: Focuses on combinatorics, including random graphs, extremal graph theory, positional games, and algorithmic graph theory. His work spans Hamilton cycles, Cayley graphs, information theory, and structural graph properties. He has contributed to over 10 peer-reviewed publications in journals like Journal of Combinatorial Theory and SIAM Journal on Discrete Mathematics . Funding & Grants: Secured research grants from the Cyprus Research Promotion Foundation and the EPSRC, including projects on extremal combinatorics and graph algorithms. Awards: Smith Prize (University of Cambridge). Outreach: Trains Cypriot Mathematical Olympiad teams, creates problems for international competitions (e.g., IMO Shortlist 2020), and co-authored a training book for high-school mathematics competitions. Teaching: Leads and teaches modules across all undergraduate years, including Real Analysis, Algebra, and Complex Analysis. Previously taught at Charles University, Warwick, and Birmingham.
Francesco Solombrino is a Full Professor at the Università del Salento, Italy, affiliated with the Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali. His academic journey includes a PhD in Applied Mathematics from Scuola Superiore di Studi Avanzati Trieste and prior roles as Full Professor at the University of Naples Federico II and researcher positions at institutions like TU Munich and the Austrian Academy of Sciences. His research focuses on Calculus of Variations, Continuum Mechanics, and Optimal Control of multi-agent systems, with over 50 publications in leading journals. Education: PhD in Applied Mathematics, Scuola Superiore di Studi Avanzati Trieste (2010) Master Degree in Mathematics, Università del Salento (2006) Research Interests: Solombrino’s work spans Rate-Independent Systems, Free Discontinuity Problems, and Geometric Measure Theory. He has contributed to advancements in mean-field control theory and mathematical modeling of material failure. Grants & Projects: He is Principal Investigator (PI) for the PRIN 2022 Project on Variational Analysis of Complex Systems and the STAR Plus project on New Perspectives in Continuum Mechanics Modeling. His research is supported by initiatives like the Compagnia di San Paolo Foundation. Labs & Collaborations: Engages with interdisciplinary teams in applied mathematics and materials science, contributing to international conferences and workshops on variational analysis and calculus of variations.
- Representation Learning
- Robotics
- Machine Learning
- +۷ مورد دیگر
Fotios Lygerakis is a doctoral student and university assistant at the Department of Cyber-Physical Systems (CPS) at Montanuniversität Leoben, Austria. His research focuses on advancing machine learning and robotics, particularly in representation learning, visuotactile fusion, and reinforcement learning for robotic manipulation. He holds a Diploma in Electrical and Computer Engineering from the Technical University of Crete (2019) and has held roles including teaching assistant at the University of Texas at Arlington, research assistant at Demokritos (Athens), and research intern at Toshiba Research Europe. His research interests span representation learning, multimodal fusion, reinforcement learning, and healthcare robotics. Notable contributions include work on CR-VAE and M2CURL, earning a Best Student Paper Award at the 2024 Ubiquitous Robotics Conference. Lygerakis actively supervises theses in areas like human-robot interaction and self-supervised learning, and teaches courses on machine learning and deep learning. Lygerakis maintains active engagement in the scientific community through reviewing for journals/conferences (e.g., IROS, IJRR), organizing workshops, and leading the Neural Coffee Reading Group. He has presented invited talks at institutions like New York University and Technical University of Crete, and his work has been featured in outlets like Computer Vision News.